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2020
09-25

python 日志 logging模块详细解析

Python 中的 logging 模块可以让你跟踪代码运行时的事件,当程序崩溃时可以查看日志并且发现是什么引发了错误。Log 信息有内置的层级——调试(debugging)、信息(informational)、警告(warnings)、错误(error)和严重错误(critical)。你也可以在 logging 中包含 traceback 信息。不管是小项目还是大项目,都推荐在 Python 程序中使用 logging。本文给大家介绍python 日志 logging模块 介绍。

1 基本使用

配置logging基本的设置,然后在控制台输出日志,

import logging
logging.basicConfig(level = logging.INFO,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
 
logger.info("Start print log")
logger.debug("Do something")
logger.warning("Something maybe fail.")
logger.info("Finish")

运行时,控制台输出,

2016-10-09 19:11:19,434 - __main__ - INFO - Start print log
2016-10-09 19:11:19,434 - __main__ - WARNING - Something maybe fail.
2016-10-09 19:11:19,434 - __main__ - INFO - Finish

logging中可以选择很多消息级别,如debug、info、warning、error以及critical。通过赋予logger或者handler不同的级别,开发者就可以只输出错误信息到特定的记录文件,或者在调试时只记录调试信息。
例如,我们将logger的级别改为DEBUG,再观察一下输出结果,

logging.basicConfig(level = logging.DEBUG,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')

控制台输出,可以发现,输出了debug的信息。

2016-10-09 19:12:08,289 - __main__ - INFO - Start print log
2016-10-09 19:12:08,289 - __main__ - DEBUG - Do something
2016-10-09 19:12:08,289 - __main__ - WARNING - Something maybe fail.
2016-10-09 19:12:08,289 - __main__ - INFO - Finish

logging.basicConfig函数各参数:
filename:指定日志文件名;
filemode:和file函数意义相同,指定日志文件的打开模式,'w'或者'a';
format:指定输出的格式和内容,format可以输出很多有用的信息,

参数:作用 

%(levelno)s:打印日志级别的数值
%(levelname)s:打印日志级别的名称
%(pathname)s:打印当前执行程序的路径,其实就是sys.argv[0]
%(filename)s:打印当前执行程序名
%(funcName)s:打印日志的当前函数
%(lineno)d:打印日志的当前行号
%(asctime)s:打印日志的时间
%(thread)d:打印线程ID
%(threadName)s:打印线程名称
%(process)d:打印进程ID
%(message)s:打印日志信息
datefmt:指定时间格式,同time.strftime();
level:设置日志级别,默认为logging.WARNNING;
stream:指定将日志的输出流,可以指定输出到sys.stderr,sys.stdout或者文件,默认输出到sys.stderr,当stream和filename同时指定时,stream被忽略;

 2 将日志写入到文件

2.2.1 将日志写入到文件

设置logging,创建一个FileHandler,并对输出消息的格式进行设置,将其添加到logger,然后将日志写入到指定的文件中,

import logging
logger = logging.getLogger(__name__)
logger.setLevel(level = logging.INFO)
handler = logging.FileHandler("log.txt")
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
 
logger.info("Start print log")
logger.debug("Do something")
logger.warning("Something maybe fail.")
logger.info("Finish")

log.txt中日志数据为,

2016-10-09 19:01:13,263 - __main__ - INFO - Start print log
2016-10-09 19:01:13,263 - __main__ - WARNING - Something maybe fail.
2016-10-09 19:01:13,263 - __main__ - INFO - Finish

2.2 将日志同时输出到屏幕和日志文件

logger中添加StreamHandler,可以将日志输出到屏幕上,

import logging
logger = logging.getLogger(__name__)
logger.setLevel(level = logging.INFO)
handler = logging.FileHandler("log.txt")
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
 
console = logging.StreamHandler()
console.setLevel(logging.INFO)
 
logger.addHandler(handler)
logger.addHandler(console)
 
logger.info("Start print log")
logger.debug("Do something")
logger.warning("Something maybe fail.")
logger.info("Finish")

可以在log.txt文件和控制台中看到,

2016-10-09 19:20:46,553 - __main__ - INFO - Start print log
2016-10-09 19:20:46,553 - __main__ - WARNING - Something maybe fail.
2016-10-09 19:20:46,553 - __main__ - INFO - Finish

可以发现,logging有一个日志处理的主对象,其他处理方式都是通过addHandler添加进去,logging中包含的handler主要有如下几种,

handler名称:位置;作用

StreamHandler:logging.StreamHandler;日志输出到流,可以是sys.stderr,sys.stdout或者文件
FileHandler:logging.FileHandler;日志输出到文件
BaseRotatingHandler:logging.handlers.BaseRotatingHandler;基本的日志回滚方式
RotatingHandler:logging.handlers.RotatingHandler;日志回滚方式,支持日志文件最大数量和日志文件回滚
TimeRotatingHandler:logging.handlers.TimeRotatingHandler;日志回滚方式,在一定时间区域内回滚日志文件
SocketHandler:logging.handlers.SocketHandler;远程输出日志到TCP/IP sockets
DatagramHandler:logging.handlers.DatagramHandler;远程输出日志到UDP sockets
SMTPHandler:logging.handlers.SMTPHandler;远程输出日志到邮件地址
SysLogHandler:logging.handlers.SysLogHandler;日志输出到syslog
NTEventLogHandler:logging.handlers.NTEventLogHandler;远程输出日志到Windows NT/2000/XP的事件日志
MemoryHandler:logging.handlers.MemoryHandler;日志输出到内存中的指定buffer
HTTPHandler:logging.handlers.HTTPHandler;通过"GET"或者"POST"远程输出到HTTP服务器

2.3 日志回滚

使用RotatingFileHandler,可以实现日志回滚,

import logging
from logging.handlers import RotatingFileHandler
logger = logging.getLogger(__name__)
logger.setLevel(level = logging.INFO)
#定义一个RotatingFileHandler,最多备份3个日志文件,每个日志文件最大1K
rHandler = RotatingFileHandler("log.txt",maxBytes = 1*1024,backupCount = 3)
rHandler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
rHandler.setFormatter(formatter)
 
console = logging.StreamHandler()
console.setLevel(logging.INFO)
console.setFormatter(formatter)
 
logger.addHandler(rHandler)
logger.addHandler(console)
 
logger.info("Start print log")
logger.debug("Do something")
logger.warning("Something maybe fail.")
logger.info("Finish")

可以在工程目录中看到,备份的日志文件,

2016/10/09  19:36               732 log.txt
2016/10/09  19:36               967 log.txt.1
2016/10/09  19:36               985 log.txt.2
2016/10/09  19:36               976 log.txt.3

 2.3 设置消息的等级

可以设置不同的日志等级,用于控制日志的输出,
日志等级:使用范围
 FATAL:致命错误
CRITICAL:特别糟糕的事情,如内存耗尽、磁盘空间为空,一般很少使用
ERROR:发生错误时,如IO操作失败或者连接问题
WARNING:发生很重要的事件,但是并不是错误时,如用户登录密码错误
INFO:处理请求或者状态变化等日常事务
DEBUG:调试过程中使用DEBUG等级,如算法中每个循环的中间状态

 2.4 捕获traceback

Python中的traceback模块被用于跟踪异常返回信息,可以在logging中记录下traceback,
代码,

import logging
logger = logging.getLogger(__name__)
logger.setLevel(level = logging.INFO)
handler = logging.FileHandler("log.txt")
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
 
console = logging.StreamHandler()
console.setLevel(logging.INFO)
 
logger.addHandler(handler)
logger.addHandler(console)
 
logger.info("Start print log")
logger.debug("Do something")
logger.warning("Something maybe fail.")
try:
 open("sklearn.txt","rb")
except (SystemExit,KeyboardInterrupt):
 raise
except Exception:
 logger.error("Faild to open sklearn.txt from logger.error",exc_info = True)
 
logger.info("Finish")

控制台和日志文件log.txt中输出,

Start print log
Something maybe fail.
Faild to open sklearn.txt from logger.error
Traceback (most recent call last):
 File "G:\zhb7627\Code\Eclipse WorkSpace\PythonTest\test.py", line 23, in <module>
 open("sklearn.txt","rb")
IOError: [Errno 2] No such file or directory: 'sklearn.txt'
Finish

也可以使用logger.exception(msg,_args),它等价于logger.error(msg,exc_info = True,_args),

logger.error("Faild to open sklearn.txt from logger.error",exc_info = True)

替换为,

logger.exception("Failed to open sklearn.txt from logger.exception")

控制台和日志文件log.txt中输出,

Start print log
Something maybe fail.
Failed to open sklearn.txt from logger.exception
Traceback (most recent call last):
 File "G:\zhb7627\Code\Eclipse WorkSpace\PythonTest\test.py", line 23, in <module>
 open("sklearn.txt","rb")
IOError: [Errno 2] No such file or directory: 'sklearn.txt'
Finish

2.5 多模块使用logging

主模块mainModule.py,

import logging
import subModule
logger = logging.getLogger("mainModule")
logger.setLevel(level = logging.INFO)
handler = logging.FileHandler("log.txt")
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
 
console = logging.StreamHandler()
console.setLevel(logging.INFO)
console.setFormatter(formatter)
 
logger.addHandler(handler)
logger.addHandler(console)
 
logger.info("creating an instance of subModule.subModuleClass")
a = subModule.SubModuleClass()
logger.info("calling subModule.subModuleClass.doSomething")
a.doSomething()
logger.info("done with subModule.subModuleClass.doSomething")
logger.info("calling subModule.some_function")
subModule.som_function()
logger.info("done with subModule.some_function")

子模块subModule.py,

import logging
 
module_logger = logging.getLogger("mainModule.sub")
class SubModuleClass(object):
 def __init__(self):
 self.logger = logging.getLogger("mainModule.sub.module")
 self.logger.info("creating an instance in SubModuleClass")
 def doSomething(self):
 self.logger.info("do something in SubModule")
 a = []
 a.append(1)
 self.logger.debug("list a = " + str(a))
 self.logger.info("finish something in SubModuleClass")
 
def som_function():
 module_logger.info("call function some_function")

执行之后,在控制和日志文件log.txt中输出,

2016-10-09 20:25:42,276 - mainModule - INFO - creating an instance of subModule.subModuleClass
2016-10-09 20:25:42,279 - mainModule.sub.module - INFO - creating an instance in SubModuleClass
2016-10-09 20:25:42,279 - mainModule - INFO - calling subModule.subModuleClass.doSomething
2016-10-09 20:25:42,279 - mainModule.sub.module - INFO - do something in SubModule
2016-10-09 20:25:42,279 - mainModule.sub.module - INFO - finish something in SubModuleClass
2016-10-09 20:25:42,279 - mainModule - INFO - done with  subModule.subModuleClass.doSomething
2016-10-09 20:25:42,279 - mainModule - INFO - calling subModule.some_function
2016-10-09 20:25:42,279 - mainModule.sub - INFO - call function some_function
2016-10-09 20:25:42,279 - mainModule - INFO - done with subModule.some_function

首先在主模块定义了logger'mainModule',并对它进行了配置,就可以在解释器进程里面的其他地方通过getLogger('mainModule')得到的对象都是一样的,不需要重新配置,可以直接使用。定义的该logger的子logger,都可以共享父logger的定义和配置,所谓的父子logger是通过命名来识别,任意以'mainModule'开头的logger都是它的子logger,例如'mainModule.sub'。
实际开发一个application,首先可以通过logging配置文件编写好这个application所对应的配置,可以生成一个根logger,如'PythonAPP',然后在主函数中通过fileConfig加载logging配置,接着在application的其他地方、不同的模块中,可以使用根logger的子logger,如'PythonAPP.Core','PythonAPP.Web'来进行log,而不需要反复的定义和配置各个模块的logger。

3 通过JSON或者YAML文件配置logging模块

尽管可以在Python代码中配置logging,但是这样并不够灵活,最好的方法是使用一个配置文件来配置。在Python 2.7及以后的版本中,可以从字典中加载logging配置,也就意味着可以通过JSON或者YAML文件加载日志的配置。

3.1 通过JSON文件配置

JSON配置文件,

{
 "version":1,
 "disable_existing_loggers":false,
 "formatters":{
 "simple":{
 "format":"%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 }
 },
 "handlers":{
 "console":{
 "class":"logging.StreamHandler",
 "level":"DEBUG",
 "formatter":"simple",
 "stream":"ext://sys.stdout"
 },
 "info_file_handler":{
 "class":"logging.handlers.RotatingFileHandler",
 "level":"INFO",
 "formatter":"simple",
 "filename":"info.log",
 "maxBytes":"10485760",
 "backupCount":20,
 "encoding":"utf8"
 },
 "error_file_handler":{
 "class":"logging.handlers.RotatingFileHandler",
 "level":"ERROR",
 "formatter":"simple",
 "filename":"errors.log",
 "maxBytes":10485760,
 "backupCount":20,
 "encoding":"utf8"
 }
 },
 "loggers":{
 "my_module":{
 "level":"ERROR",
 "handlers":["info_file_handler"],
 "propagate":"no"
 }
 },
 "root":{
 "level":"INFO",
 "handlers":["console","info_file_handler","error_file_handler"]
 }
}

通过JSON加载配置文件,然后通过logging.dictConfig配置logging,

import json
import logging.config
import os
 
def setup_logging(default_path = "logging.json",default_level = logging.INFO,env_key = "LOG_CFG"):
 path = default_path
 value = os.getenv(env_key,None)
 if value:
 path = value
 if os.path.exists(path):
 with open(path,"r") as f:
 config = json.load(f)
 logging.config.dictConfig(config)
 else:
 logging.basicConfig(level = default_level)
 
def func():
 logging.info("start func")
 
 logging.info("exec func")
 
 logging.info("end func")
 
if __name__ == "__main__":
 setup_logging(default_path = "logging.json")
 func()

3.2 通过YAML文件配置

通过YAML文件进行配置,比JSON看起来更加简介明了,

version: 1
disable_existing_loggers: False
formatters:
 simple:
 format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
handlers:
 console:
 class: logging.StreamHandler
 level: DEBUG
 formatter: simple
 stream: ext://sys.stdout
 info_file_handler:
 class: logging.handlers.RotatingFileHandler
 level: INFO
 formatter: simple
 filename: info.log
 maxBytes: 10485760
 backupCount: 20
 encoding: utf8
 error_file_handler:
 class: logging.handlers.RotatingFileHandler
 level: ERROR
 formatter: simple
 filename: errors.log
 maxBytes: 10485760
 backupCount: 20
 encoding: utf8
loggers:
 my_module:
 level: ERROR
 handlers: [info_file_handler]
 propagate: no
root:
 level: INFO
 handlers: [console,info_file_handler,error_file_handler]

通过YAML加载配置文件,然后通过logging.dictConfig配置logging,

import yaml
import logging.config
import os
 
def setup_logging(default_path = "logging.yaml",default_level = logging.INFO,env_key = "LOG_CFG"):
 path = default_path
 value = os.getenv(env_key,None)
 if value:
 path = value
 if os.path.exists(path):
 with open(path,"r") as f:
 config = yaml.load(f)
 logging.config.dictConfig(config)
 else:
 logging.basicConfig(level = default_level)
 
def func():
 logging.info("start func")
 
 logging.info("exec func")
 
 logging.info("end func")
 
if __name__ == "__main__":
 setup_logging(default_path = "logging.yaml")
 func()

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